[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-291010-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-291010-en":131},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","a-machine-learning-approach-for-analyzing-sexual-satisfaction-based-on-psychological-features","A machine learning approach for analyzing sexual satisfaction based on psychological features","","Machine learning techniques are applied to analyze and classify sexual satisfaction by integrating psychological and behavioral variables. The study classifies sexual satisfaction for women (n=503) and men (n=342) using supervised learning on data from Colombian adults collected via snowball sampling. Inputs include marital status, sociosexuality, sexual drive, sexual functioning, and personality traits. Results identify XGBoost as best for women, ANN for men, with sexual functioning and drive as strongest predictors. Additional predictors differ by gender, including psychopathy for men and conscientiousness for women.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/a-machine-learning-approach-for-analyzing-sexual-satisfaction-based-on-psychological-features/291010/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/a-machine-learning-approach-for-analyzing-sexual-satisfaction-based-on-psychological-features/291010.png","ImageObject",300,407,{"name":42,"@type":43},"Evangeline","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-29","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",15,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What was the study’s main goal?","Question",{"text":63,"@type":64},"To classify sexual satisfaction in women and men using machine learning models based on psychological and behavioral features.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"Which algorithms performed best for each gender?",{"text":68,"@type":64},"XGBoost provided the best classification results for women, while Artificial Neural Networks (ANN) performed best for men.",{"name":70,"@type":61,"acceptedAnswer":71},"Which features were most important predictors?",{"text":72,"@type":64},"Across both groups, sexual functioning and sexual drive were the most significant predictors of sexual satisfaction.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},291010,1789643174,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,127],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":124,"doc_module":4,"doc_module_name":25,"category_name":125,"show_sort_weight":124,"slug":126},10,"Lifestyle","lifestyle",{"id":128,"doc_module":4,"doc_module_name":25,"category_name":129,"show_sort_weight":97,"slug":130},19,"General","general",{"code":4,"msg":82,"data":132},{"doc_id":79,"user_id":133,"nickname":42,"user_avatar":134,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":124,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":80,"read_time":144,"preview_page_count":4},13056703019662,"https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188","A machine learning approach for analyzing sexual satisfaction based on psychological features  \nDuban Romero1 · Lihki Rubio1 · David L. Rodrigues2 · Moisés Mebarak1  \nAccepted: 4 October 2024 © The Author(s) 2024  \nAbstract  \nThe emergence of machine learning techniques has revolutionized various fields, helping to shed light into the complexities of human sexuality and address sexuality-related problems. The present study aimed to classify sexual satisfaction in both women (n = 503) and men (n = 342), who completed a digital survey aimed at Colombian adults based on a snowball sampling. Collected data were analyzed using several supervised learning algorithms where inputs included marital status, sociosexuality, sexual drive, sexual functioning, and personality traits. The results showed that the XGBoost model provided best classification results for sexual satisfaction in women, while the Artificial Neural Networks (ANN) had the best performance in classifying sexual satisfaction in men. In both groups, sexual functioning and sexual drive were the most significant predictors of sexual satisfaction. Traits such as extraversion, narcissism, machiavellianism, and sociosexual behavior had a lesser importance. Lastly, psychopathy emerged as a significant predictor of men's sexual satisfaction, whereas conscientiousness emerged as a significant predictor of women's satisfaction. This study provides a technological tool to classify sexual satisfaction using Machine Learning models, in addition, provide, in terms of entropy, variables with the greatest influence based on the data and predictions.  \nKeywords Supervised learning · Sexual satisfaction · Sexual functioning · Personality · Dark Triad  \nIntroduction  \nMachine Learning Models (MLMs) have been widely used in different fields of engineering and science with positive results (Karpatne et al., 2018; Reich & Barai, 1999; Zhong et al., 2021) . These models have demonstrated remarkable capabilities to solve problems related with digital image processing, natural language processing, financial prediction, among others (Fischer & Krauss, 2018; Lundervold & Lundervold, 2019; Otter et al., 2020) . The emergence of machine learning techniques has revolutionized various fields, including shedding light on the complexities of human sexuality, and helping to address sexuality-related problems. For example, studies have already implemented machine learning techniques to identify user satisfaction  \n􀀍 Duban Romero [rduban@uninorte.edu.co](rduban@uninorte.edu.co)  \n1 Universidad del Norte, Barranquilla, Colombia  \n2 Iscte-Instituto Universitário de Lisboa, CIS-Iscte, Lisbon, Portugal  \nwith dating apps (Vera et al., 2023), detect sexual harassment (Alawneh et al., 2021), and sexual grooming (Ngejane et al., 2018), forecast future sexual behaviors in HIV-positive patients (Andresen et al., 2022), predict sexual desire (Vowels et al., 2021), or predict potential extradyadic behaviors between romantic partners (Vowels et al., 2022a, 2022b) . It is imperative to implement machine learning algorithms to attain a deeper understanding of certain issues, such as sexual satisfaction, since these techniques allow to analyze large volumes of data and to identify patterns that traditional statistics would not be able to detect.  \nResearch has demonstrated that sexual satisfaction can profoundly impact individuals' quality of life, influencing mental health outcomes (Abdollahi et al., 2021; Carcedo et al., 2020; Talayizadeh & Bakhtiyarpour, 2016). For instance, sexual satisfaction in adolescents has been correlated with lower levels of anxiety, and in young adults has been correlated with better psychological health and reduced levels of depression, particularly within the context of romantic relationships. The prevalence of low sexual satisfaction is higher among people diagnosed with depression, such that 63% of men and 82% of women report being sexually  \n1 3  \ndissatisfied (Goncalves et al., 2023) .","cbCaioguZAGvLakS","https://ap.wps.com/l/cbCaioguZAGvLakS","pdf",1221241,"English","# Abstract\n# Keywords\n# Introduction","[{\"question\":\"What was the study’s main goal?\",\"answer\":\"To classify sexual satisfaction in women and men using machine learning models based on psychological and behavioral features.\"},{\"question\":\"Which algorithms performed best for each gender?\",\"answer\":\"XGBoost provided the best classification results for women, while Artificial Neural Networks (ANN) performed best for men.\"},{\"question\":\"Which features were most important predictors?\",\"answer\":\"Across both groups, sexual functioning and sexual drive were the most significant predictors of sexual satisfaction.\"}]","A machine learning approach for analyzing sexual satisfaction based on psychological features | PDF",25]